Estimation of directions of arrival (DOAs) of wide-sense stationary (WSS) signals using the data sampled by an array of sensors is an important task in many fields such as sonar, radar, seismology, radio astronomy, and neurophysiology. The data sampled by an \(L\) -element uniform linear array (ULA) that has an intersensor spacing of \(\lambda /2,\)  where \(\lambda \)  is the wavelength of the WSS signals impinging on the array, can be used to resolve up to \(L-1\)  DOAs [1]}, [2]}. There exist augmentable sparse arrays for which the number of resolvable DOAs exceeds the number of sensors. An \(L_S\) -element augmentable sparse array that has an aperture of \((L_F-1)\lambda /2\)  can resolve \(L_A-1\)  DOAs where \(L_F\ge L_A\ge L_S\) . Sparse arrays where \(L_A=L_F\)  are called fully augmentable arrays and sparse arrays where \(L_S<L_A<L_F\)  are called partially augmentable arrays. Minimum redundant arrays [3]} and nested arrays [4]} are examples of fully augmentable sparse arrays. Semi-coprime arrays [5]} are examples of partially augmentable sparse arrays. Coprime arrays [6]} may be partially or fully augmentable based on the design parameters.
